python3：如何使用CSFR从网站上获取研究结果？

Question

我试图搜集一个网站的研究成果，该网站列出了法国众贷金融科技公司：https://www.orias.fr/web/guest/search

手动操作时，我在单选按钮中选择（IFP），然后它为我提供13个结果页，每页10个结果。每一个结果都有一个超链接，我也想从中得到信息，进入最后的表格。你知道吗

我的主要问题似乎来自CSRF，在结果地址中，有： 认证=8mxk0SsK 因此，我不能简单地通过将链接中的“p=2”更改为“p=13”来循环浏览结果页： https://www.orias.fr/search?p_auth=8mxk0SsK&p_p_id=intermediaryDetailedSearch_WAR_oriasportlet&p_p_lifecycle=1&p_p_state=normal&p_p_mode=view&p_p_col_id=column-1&p_p_col_count=1&_intermediaryDetailedSearch_WAR_oriasportlet_myaction=fullSearch

如果我尝试手动使用VPN，wesite地址将变为“稳定”： https://www.orias.fr/search?p_p_id=intermediaryDetailedSearch_WAR_oriasportlet&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-1&p_p_col_count=1&_intermediaryDetailedSearch_WAR_oriasportlet_d-16544-p=1&_intermediaryDetailedSearch_WAR_oriasportlet_implicitModel=true&_intermediaryDetailedSearch_WAR_oriasportlet_spring_render=searchResult

所以我试着在python代码中使用它：

    import requests
    from bs4 import BeautifulSoup

    k = 1
    % test k from 1 to 13


    url = "http://www.orias.fr/search?p_p_id=intermediaryDetailedSearch_WAR_oriasportlet&p_p_lifecycle=0&p_p_state=normal&p_p_mode=view&p_p_col_id=column-1&p_p_col_count=1&_intermediaryDetailedSearch_WAR_oriasportlet_d-16544-p="  + str(k) + "&_intermediaryDetailedSearch_WAR_oriasportlet_implicitModel=true&_intermediaryDetailedSearch_WAR_oriasportlet_spring_render=searchResult"
    response = requests.get(url, proxies=proxies) # 200 ment it went through    
    soup = BeautifulSoup(response.text, "html.parser")

    table = soup.find('table', attrs={'class':'table table-condensed table-striped table-bordered'})
    table_rows = table.find_all('tr')

    l = []
    for tr in table_rows:
        td = tr.find_all('td')
        row = [tr.text for tr in td]
        l.append(row)

它不像在web浏览器中那样工作，它只是提供一个页面，就好像没有请求任何结果一样。你知道怎么做吗？你知道吗

Answer 1

所以这里是完整的代码，同时也考虑到“每个结果都有一个超链接，我也想从中获取信息到最终的表中”。因此，对于每家公司，我更新和标题，然后刮注册或删除日期。可能有一种更优雅的方式来呈现代码。。。你知道吗

from bs4 import BeautifulSoup as bs
import requests, re, math
import pandas as pd

headers = {
    'Content-Type': 'application/x-www-form-urlencoded',
    'User-Agent': 'Mozilla/5.0',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
    'Referer': 'https://www.orias.fr/web/guest/search'
}

params = [['p_p_id', 'intermediaryDetailedSearch_WAR_oriasportlet'],
    ['p_p_lifecycle', '0'],
    ['p_p_state', 'normal'],
    ['p_p_mode', 'view'],
    ['p_p_col_id', 'column-1'],
    ['p_p_col_count', '1'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_d-16544-p', '1'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_implicitModel', 'true'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_spring_render', 'searchResult']]

data = {
  'searchString': '',
  'address': '',
  'zipCodeOrCity': '',
  '_coa': 'on',
  '_aga': 'on',
  '_ma': 'on',
  '_mia': 'on',
  '_euIAS': 'on',
  'mandatorDenomination': '',
  'wantsMandator': 'no',
  '_cobsp': 'on',
  '_mobspl': 'on',
  '_mobsp': 'on',
  '_miobsp': 'on',
  '_bankActivities': '1',
  '_euIOBSP': 'on',
  '_cif': 'on',
  '_alpsi': 'on',
  '_cip': 'on',
  'ifp': 'true',
  '_ifp': 'on',
  'submit': 'Search'
}

p = re.compile(r'(\d+)\s+intermediaries found')

def webdata(soup):
    parsed_table = soup.find_all('table')[0]
    dataweb = [[td.a['href'] if td.find('a') else 
             ''.join(td.stripped_strings)
             for td in row.find_all('td')]
            for row in parsed_table.find_all('tr')]
    dfweb = pd.DataFrame(dataweb[1:], columns=['SIREN','ID','website','category','zipcode','city','website2'])
    dfweb = dfweb.loc[:,['ID','website']]
    dfweb.ID = dfweb.ID.astype(int)
    return dfweb

with requests.Session() as s:
    r= requests.post('https://www.orias.fr/search', headers=headers, params= params, data=data)
    soup = bs(r.content, 'lxml') 
    num_results = int(p.findall(r.text)[0])
    results_per_page = 20
    num_pages = math.ceil(num_results/results_per_page)
    df = pd.read_html(str(soup.select_one('.table')))[0]
    dfweb = webdata(soup)
    df = pd.merge(df,dfweb, on='ID')

    for i in range(2, num_pages + 1):
        params[6][1] = str(i)
        r= requests.post('https://www.orias.fr/search', headers=headers, params= params, data=data)
        soup = bs(r.content, 'lxml') 
        df_next = pd.read_html(str(soup.select_one('.table')))[0]
        dfweb = webdata(soup)
        df_next  = pd.merge(df_next ,dfweb, on='ID')
        df = pd.concat([df, df_next])

df.drop('Unnamed: 6', axis = 1, inplace = True)
df = df.reset_index(drop=True) 

# list the ORIAS identity number given to frims
# get le last 6 character of the link, but last is a space
df['oriasID'] = df.website.apply(lambda x: x[-7:][:6])
# remove = sign, non digit
df['oriasID'] = df.oriasID.apply(lambda y: ''.join(i for i in y if i.isdigit()))


# new parameters
def paramsub(IDi):
    return [['p_p_id', 'intermediaryDetailedSearch_WAR_oriasportlet'],
    ['p_p_lifecycle', '1'],
    ['p_p_state', 'normal'],
    ['p_p_mode', 'view'],
    ['p_p_col_id', 'column-1'],
    ['p_p_col_count', '1'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_myaction', 'viewDetails'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_partyId', IDi]]


df['date in'] = False
df['date out'] = False
with requests.Session() as s:
    for i in df.index:
        IDi = df.loc[i,'oriasID']
        r= requests.post('https://www.orias.fr/search', headers=headers, params= paramsub(IDi), data=data)
        soup = bs(r.content, 'lxml') 
        # keep data inside blocint3 is "(IFP)" is in the text
        for rowi in soup.find_all('div',{'class':'blocint3'}):
            if 'IFP' in rowi.text:
                if 'Deleted' in rowi.text:
                    # identify date
                    df.loc[i,'date out'] = re.search(r'\d{2}-\d{2}-\d{4}', rowi.text).group()
                elif 'Registered' in rowi.text:
                    df.loc[i,'date in'] = re.search(r'\d{2}-\d{2}-\d{4}', rowi.text).group()

# dates to date format
df['date in'] = pd.to_datetime(df['date in'], format="%d-%m-%Y", errors='coerce')
df['date out'] = pd.to_datetime(df['date out'], format="%d-%m-%Y", errors='coerce')

# sort by dates
df = df.sort_values(by='date out',ascending=True)
df = df.sort_values(by='date in',ascending=True)

df = df.reset_index(drop=True) 
# export
df.to_csv('20190817_ORIAS_in_out.csv')

Answer 2

我会在循环期间更改post请求中的page参数。做一个初始请求以找出页数

from bs4 import BeautifulSoup as bs
import requests, re, math
import pandas as pd

headers = {
    'Content-Type': 'application/x-www-form-urlencoded',
    'User-Agent': 'Mozilla/5.0',
    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3',
    'Referer': 'https://www.orias.fr/web/guest/search'
}

params = [['p_p_id', 'intermediaryDetailedSearch_WAR_oriasportlet'],
    ['p_p_lifecycle', '0'],
    ['p_p_state', 'normal'],
    ['p_p_mode', 'view'],
    ['p_p_col_id', 'column-1'],
    ['p_p_col_count', '1'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_d-16544-p', '1'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_implicitModel', 'true'],
    ['_intermediaryDetailedSearch_WAR_oriasportlet_spring_render', 'searchResult']]

data = {
  'searchString': '',
  'address': '',
  'zipCodeOrCity': '',
  '_coa': 'on',
  '_aga': 'on',
  '_ma': 'on',
  '_mia': 'on',
  '_euIAS': 'on',
  'mandatorDenomination': '',
  'wantsMandator': 'no',
  '_cobsp': 'on',
  '_mobspl': 'on',
  '_mobsp': 'on',
  '_miobsp': 'on',
  '_bankActivities': '1',
  '_euIOBSP': 'on',
  '_cif': 'on',
  '_alpsi': 'on',
  '_cip': 'on',
  'ifp': 'true',
  '_ifp': 'on',
  'submit': 'Search'
}

p = re.compile(r'(\d+)\s+intermediaries found')

with requests.Session() as s:
    r= requests.post('https://www.orias.fr/search', headers=headers, params= params, data=data)
    soup = bs(r.content, 'lxml') 
    num_results = int(p.findall(r.text)[0])
    results_per_page = 20
    num_pages = math.ceil(num_results/results_per_page)
    df = pd.read_html(str(soup.select_one('.table')))[0]

    for i in range(2, num_pages + 1):
        params[6][1] = str(i)
        r= requests.post('https://www.orias.fr/search', headers=headers, params= params, data=data)
        soup = bs(r.content, 'lxml') 
        df_next = pd.read_html(str(soup.select_one('.table')))[0]
        df = pd.concat([df, df_next])

df.drop('Unnamed: 6', axis = 1, inplace = True)
df.reset_index(drop=True) 

---

检查：

print(len(df['Siren Number'].unique()))
#245